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Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images
This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-com...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8000794/ https://www.ncbi.nlm.nih.gov/pubmed/33808967 http://dx.doi.org/10.3390/s21061994 |
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author | Ma, Qian Han, Wenting Huang, Shenjin Dong, Shide Li, Guang Chen, Haipeng |
author_facet | Ma, Qian Han, Wenting Huang, Shenjin Dong, Shide Li, Guang Chen, Haipeng |
author_sort | Ma, Qian |
collection | PubMed |
description | This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures. |
format | Online Article Text |
id | pubmed-8000794 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80007942021-03-28 Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images Ma, Qian Han, Wenting Huang, Shenjin Dong, Shide Li, Guang Chen, Haipeng Sensors (Basel) Article This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures. MDPI 2021-03-12 /pmc/articles/PMC8000794/ /pubmed/33808967 http://dx.doi.org/10.3390/s21061994 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ma, Qian Han, Wenting Huang, Shenjin Dong, Shide Li, Guang Chen, Haipeng Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images |
title | Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images |
title_full | Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images |
title_fullStr | Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images |
title_full_unstemmed | Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images |
title_short | Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images |
title_sort | distinguishing planting structures of different complexity from uav multispectral images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8000794/ https://www.ncbi.nlm.nih.gov/pubmed/33808967 http://dx.doi.org/10.3390/s21061994 |
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